Abstract

Background: Asbestos-related diseases remain a significant occupational health problem in much of the world. Evidence of asbestos exposure is not always apparent in routine clinical evaluations of potentially exposed individuals, particularly in the initial stages of pathogenesis, so convenient biomarkers of exposure could be useful in the early identification of individuals at risk.

Methods and findings: Based on prior research, we examined serum levels of kinesin family proteins (KIF5A and KIF18A) and p53 autoantibodies in 198 asbestosexposed workers and 164 unexposed controls. Exposed individuals were significantly more likely to have increased KIF5A and decreased KIF18A compared to the unexposed controls; p53 autoantibodies and exposure were unrelated.

Conclusion: Serum kinesins may be useful biomarkers of asbestos exposure.

Keywords

Serum KIF5A; KIF18A; p53 Autoantibody;
Biomarkers; Asbestos

Introduction

The WHO estimates that 125 million people are
occupationally exposed to asbestos worldwide and OSHA has
estimated that as many as 1.3 million workers in construction
and general industry face significant exposures to asbestos on
the job in the U.S. [1,2]. Asbestos exposure is known to result
in a number of negative health effects, many of which have
long latency periods of up to 20-40 years, including cancer, and
there has been no evidence of a threshold for the carcinogenic
effect of asbestos [3,4]. The WHO has also raised its estimates
of the global burden of asbestos related disease to 107,000
annual deaths primarily from asbestos related lung cancer,
mesothelioma, and asbestosis [1]. Given the large population
occupationally exposed to asbestos, potential biomarkers of
exposure and/or of early effect for cancer risk from asbestos
could have a large effect on the burden of disease by
identifying individuals at the highest risk for malignancy who
could be targeted for more aggressive intervention. Based on
prior studies, it was hypothesized that certain members of the
Kinesin Family Proteins (KIFs) and p53 autoantibodies may be
potential biomarkers of asbestos exposure and/or related
cancer risk.

The kinesin superfamily of proteins (KIFs) currently includes
45 different proteins classified into 14 families [5]. KIFs are a
conserved class of microtubule-dependent molecular motor
proteins that have ATPase activity and motion characteristics.
Different subtypes of KIFs may participate in different cellular
functions, but KIF5s and KIF18s primarily participate in mitosis.
While studies have not been reported identifying KIF18A and
KIF5A in cell culture experiments as specific targets for
asbestos interaction or effect, it is plausible that they could be
affected by alterations induced by asbestos exposure in the
other proteins that regulate mitosis. Abnormal kinesin
expression can alter the equal distribution of genetic materials
between daughter cells during cell mitosis. This may occur due
to chromosome hyper condensation, aberrant spindle
formation, anaphase bridges, defective cytokinesis, aneuploidy
and mitotic arrest [6]. The resulting loss or gain of genetic
material due to the dysfunctional mitotic process can lead to a
number of defects in the daughter cells which can promote
carcinogenesis and/or the progression of aggressive behavior
of the corresponding tumor cells [7,8].

The TP53 tumor suppressor gene is the most common site
identified for genetic mutations in human cancers, which often
causes an increase in the stability of mutant p53 protein, leading to its accumulation in cancer cells [9,10]. These
mutations can occur early in the carcinogenic process and
often may have a molecular signature based on the type of
cancer and exposure linked to that cancer, which could make
p53 an attractive biomarker of early effect [9]. The inactivation
of p53 proteins and accumulation of both mutant and wild
type p53 proteins in the tissue and serum can lead to the
production of p53 autoantibodies, and in fact there is a close
correlation between serum p53 autoantibodies and p53
overexpression in corresponding tissues [11,12]. Serum p53
autoantibodies have been found in patients with a number of
pre-malignant diseases and cancers and in workers exposed to
occupational carcinogens including asbestos before any clinical
evidence of malignancy [10,11,13-15].

This study examined the potential relationship of asbestos
exposure to alterations in KIF5A and KIF18A serum
concentrations and p53 autoantibody serum concentrations to
determine if they may be potential biomarkers of asbestos
exposure for purposes of early detection and prevention.

Methods

Cohort

Information and samples from asbestos-exposed workers
and unexposed controls were provided by the University of
Perugia Occupational/Environmental Medicine Clinic for the
years 2007-2011. The asbestos-exposed workers were
recruited by the Italian National Institute for Insurance against
Accidents at Work (Istituto Nazionale per L’Assicurazione
contro gli Infortuni sul Lavoro-INAIL). Information was
collected on age, gender, years of asbestos exposure, and
smoking history of these workers. They were evaluated for
asbestos-related disease by chest radiography, spirometry and
diffusion capacity (DLCO), and 198 of these exposed workers
were deemed to not have evidence of disease. These patients
had the following characteristics: average age=62 with a
range=36-83 (1 in his 30s; 6 in their 40s; 52 in their 50s; 94 in
their 60s; 38 in their 70s; 8 in their 80s); all white males;
average asbestos exposure=26 years (range=11-38 years); 32%
non-smokers, 49% ex-smokers, and 19% current smokers (52%
of whom average 1-14 cigarettes/day, 40% of whom average
15-24 cigarettes/day, and 8% of whom average more than 24
cigarettes/day). During the same period, normal, healthy,
workers without known asbestos exposure were recruited at
the University of Perugia hospital and out-patient clinics and
164 were selected as controls based on similar age, gender,
and demographic area. Information was also collected on their
occupation and smoking history. Based upon occupational
histories all were deemed to have had no likely exposure to
asbestos or other workplace carcinogens. They had the
following characteristics: average age=57 with a range=21-99
(7 in their 20s; 13 in their 30s; 35 in their 40s; 51 in their 50s;
13 in their 60s; 15 in their 70s; 23 in their 80s; 3 in their 90s);
all white males; 34% non-smokers, 37% ex-smokers, and 29%
current smokers (48% of whom average 1-14 cigarettes/day,
35% of whom average 15-24 cigarettes/day, and 17% of whom
average more than 24 cigarettes/day).

Laboratory Procedures

Serum samples were thawed and analyzed for the presence
of KIF5A and KIF18A proteins by commercially available ELISAs
(LS-F8011 and LS-F8012; Life Span Biosciences, Seattle, WA). In
both cases, the assays are quantitative sandwich ELISAs
utilizing microtiter plates pre-coated with a monoclonal
antibody specific for the particular KIF. After incubation of the
sample, a biotin-conjugated polyclonal antibody specific for
the particular KIF is added followed by avidin-conjugated
horseradish peroxidase and 3,3’,5,5’-tetramethylbenzidine
substrate solution. The color change is measured
spectrophotometrically at 450 nm and is converted into the
concentration of KIF in the sample by comparison to a
standard curve generated from known concentrations of
purified KIF protein (run in duplicate) on each plate. The KIF
concentration of each sample/individual was then compared
to a cutoff level to determine positive or negative status for
altered protein concentrations. The optimal cutoff level for
KIF5A and KIF18A protein concentrations was explored via
several methods including: the mean KIF5A level among the
unexposed individuals plus 2 standard deviations; the mean
KIF18A level among the unexposed individuals minus 1
standard deviation, due to a large standard deviation (greater
than half the mean value which would result in a negative
concentration) among control KIF18A levels; the mean KIF18A
level among the unexposed individuals plus 1 standard
deviation; and empirically determined cutoffs utilizing ROC
analyses. The direction of the cutoffs explored is due to the
observed increase in serum KIF5A levels and decreased serum
KIF18A levels observed in a subset of asbestos cancer patients
from an occupationally exposed Finnish asbestosis cohort
reported previously, as well as the observed increases in
KIF18A expression reported in the literature above [16-20].
These assays have been demonstrated to be highly
reproducible and to have high sensitivity (LLD<118 pg/mL) and
specificity (no cross-reactivity between each specific KIF and
known analogues). The manufacturer reported intra-assay
coefficient of variation (CV) is <10% and inter-assay CV is
<12%. Finally, from the ELISA results comparisons can be made
between the KIF levels found in serum and exposure to
asbestos as described below.

Serum samples were thawed and analyzed for the presence
and concentration of p53 autoantibodies by commercially
available ELISA (DIA-0302-1; Dianova, Hamburg, Germany).
The quantitative assay utilizes microtiter plates pre-coated
with recombinant human wild-type p53 protein. After
incubation of the sample diluted 1:100, the plate is washed to
remove any unbound material and a horseradish peroxidaseconjugated
purified goat anti-human polyclonal antibody is
added to the wells which binds to any captured human p53
antibody. Following incubation and a wash step a chromogenic
substrate is added to the wells. The horseradish peroxidase
catalyzes the conversion of the chromogenic substrate 3,3’,
5,5’-tetramethylbenzidine from a colorless solution to a blue
solution (or yellow after the addition of hydrochloric acid
stopping reagent), the intensity of which is proportional to the
amount of human p53 antibody in the test sample. The color
change is measured spectrophotometrically at 450 nm and is converted into the concentration of p53 antibody in the
sample by comparison to a standard curve generated from
known concentrations of purified anti-human p53 antibody
(run in duplicate) on each plate. The p53 antibody
concentration of each sample was then compared to a cutoff
level calculated from the standard curve on each plate to
determine positive or negative status for p53 autoantibody
expression and also from empirically determined cutoffs
utilizing ROC analyses. The calculated Intra-Assay CV was
6.87% and the calculated Inter-Assay CV was 22%. Finally, from
the ELISA results comparisons can be made between the p53
autoantibody levels found in serum and asbestos exposure.

Data Analysis

Data management and statistical analyses were performed
using Microsoft Excel 2010 and SAS version 9.4. Distributions
of KIF5A, KIF18A, and p53 autoantibody levels (continuous)
were evaluated for normality and Spearman correlations were
assessed between the continuous biomarkers, continuous
exposure (years) and age. Individuals with missing variables
were dropped from the analysis. Mean levels of each potential
biomarker were compared in relation to asbestos exposure via
parametric and non-parametric analyses where appropriate as
well as via receiver operating characteristic (ROC) analysis.
ROC analysis is a method of analyzing the predictive or
discriminatory performance of a potential biomarker or
diagnostic test by plotting the sensitivity (true positive rate)
against 1-specificity (the false positive rate) for a range of
potential cut-points. From this plot one can determine the
optimal cut point for each biomarker to differentiate between
a binary outcome, in this case exposed and unexposed
individuals, utilizing various methods including Youden’s
statistic and Euclidian distance from the perfect classifier
(point 0, 1) [21]. Univariate logistic regression models for each
biomarker on exposure status were run for the ROC analysis.
Multivariable logistic regression models were also run
including all three biomarkers and the covariates of age and
smoking status as well as interaction terms for each covariate
and the potential biomarkers of interest to explore their
association with exposure status. Additionally, KIF5A, KIF18A,
and p53 autoantibody status (Positive/Negative) were each
assessed as the outcome in separate logistic regression models
(SAS Proc Logistic) based upon each subject’s status of
asbestos exposure (Exposed/Unexposed) or alternatively
based upon each subject’s duration of asbestos exposure
(years). In each logistic regression model, we additionally
explored the effects of age, smoking status (current/former/
never), and the other potential biomarkers.

Results

General descriptive statistics on the cohort including
biomarker expression levels, asbestos exposure years, and the
measured covariates of age and smoking status are presented
in Table 1. KIF5A, KIF18A, and p53 autoantibodies were found
to be log-normally distributed by statistical tests for normality
including the Shapiro-Wilk (p<0.0001), Kolmogorov-Smirnov
(p<0.01) and Anderson-Darling (p<0.005). Therefore potential
differences in serum biomarker levels between exposed and
unexposed individuals were assessed on log-transformed
biomarker values and via the non-parametric Wilcoxon ranksum
test. Exposed individuals were significantly older than
control individuals (p<0.0001) and smoking status was also
different between exposed and unexposed individuals
(p=0.0335) (Table 1).

Exposed individuals had significantly higher mean levels of
KIF5A (p<0.0001), significantly lower mean levels of KIF18A
(p=0.0008), but no difference in mean levels of p53
autoantibodies (p=0.766) in serum compared to the unexposed individuals in non-parametric Wilcoxon bivariate
analyses. ANOVA results showed that smoking status was not
significantly associated with KIF5A (p=0.6404) or p53
autoantibody serum concentrations (p=0.8294), but was
associated with KIF18A serum concentrations (p=0.0386), with
lower KIF18A concentrations in ex-smokers than current
smokers, and age (p=0.0002), with ex-smokers significantly
older than current or never smokers.

The ROC analysis for p53 autoantibody expression
confirmed that it was not significant as a predictor of exposure
status (p=0.6052) and no cut point could be determined that
adequately distinguished exposed from unexposed individuals
(Figure 1).

Figure 1: p53 AAbs ROC curve.

Both KIF5A and KIF18A were highly significant predictors of
exposure status (p<0.0001) that performed reasonably well at
distinguishing exposed from unexposed individuals as shown
in Figures 2 and 3 and allowed determination of optimal cut
points to distinguish exposed from unexposed
individuals(Figure 2 and 3).

Figure 2: KIF5A ROC curve.

Figure 3: KIF18A ROC curve.

Based upon each cut point determined for the kinesin
biomarkers from ROC analyses, individuals were classified as
positive or negative for KIF5A and KIF18A, which then served
as the outcome in multivariable logistic regression models. The
optimal cut point using the Youden’s statistic method
corresponded to 0.681 ng/mL for KIF5A and 526.399 ng/mL for
KIF18A. The optimal cut point using the Euclidean distance
method corresponded to 0.901 ng/mL for KIF5A and 361.436
ng/mL for KIF18A, demonstrating heterogeneity among
methods of selecting the optimal cut point. Due to this
heterogeneity both cut points were used in assessing the
associations of these binary biomarkers to asbestos exposure
in multivariable models.

In multivariable models assessing KIF5A positive status as
defined by the Youden’s statistic cut point, only exposure status (positive/negative) and age were statistically significant,
both at p<0.0001. Exposed individuals had 7.02 times the odds
of positive KIF5A status compared to controls, while each
additional year of age had 0.954 times the odds of positive
KIF5A status (Table 3). In a model using continuous exposure
instead of binary exposure status each additional year of
exposure had 1.07 times the odds of positive KIF5A status. In
multivariable models assessing KIF18A positive status as
defined by the Youden’s statistic cut point, exposure status
(positive/negative) (p=0.0003) and age (p<0.0001) and the
interaction term for status and age (p=0.0016) were
statistically significant. Exposed individuals had significantly
lower odds of positive KIF18A status compared to controls at
the 10th, 25th and 50th percentiles of the age distribution,
while exposure status wasn’t significantly associated with
KIF18a status at the 75th and 90th percentiles of age (Figure
4). Results using continuous exposure were similar with
significantly lower odds of positive KIF18A status for each year
of asbestos exposure at the 10th, 25th, and 50th percentiles of
the age distribution and a non-significant association at the
75th and 90th percentiles of age. Multivariable modeling
results were similar using biomarker status for KIF5A and
KIF18A defined by the Euclidean distance method. Model fit
was better for the Youden’s statistic method compared to the
Euclidean distance method as measured by area under the
curve for KIF5A (c=0.767 vs c=0.704) and about equal for KIF18A (c=0.747 vs c=0.746). For this reason the Youden’s
statistic defined models were selected as the final models. In
multivariable models assessing p53 autoantibody status
(positive/negative) as defined by the assay protocol, neither
exposure (p=0.89), ex-smoker status (p=0.30), current smoker
(p=0.41) status, or age (p=0.13) were significant.

Models 1-3 use continuous
biomarkers; Model 4 uses dichotomous biomarkers defined by Youden's Index. KIF18A and Age had a significant interaction and
therefore simple OR is not presented, Figure 5 presents ORs for KIF18A by various ages. $ Exposure and Age had a significant
interaction (p=0.0016) and therefore simple OR is not presented, Figure 4 presents ORs for Asbestos Exposure by various ages.
All Models (n=335) as 27 individuals with missing data were dropped.

In multivariable logistic regression models of exposure
status (positive/negative) including all three biomarkers (continuous) as well as the covariates of age and smoking only
KIF5A, KIF18A, age, and the interaction term for KIF18A and
age were significant (all p<0.0001) (Table 3). Individuals with 1
ng/mL higher KIF5A serum levels had 1.87 times the odds of
asbestos exposure, while individuals with higher KIF18A serum
levels had lower odds of asbestos exposure at younger ages,
but there was non-significant association or increased odds of
exposure at older ages (Figure 5). These findings were
consistent with results from models examining KIF5A or KIF18A
alone and with the associations from models of each
biomarker as the outcome of interest.

Figure 5: Odds ratios of asbestos exposure for KIF18A serum
concentrations at the 10th, 25th, 50th, 75th, and 90th age
percentiles. Odds Ratio is for a 1 ng/dL increase in serum
KIF18A concentration.

Similar final models and significance levels were obtained
utilizing dichotomous biomarkers as defined by either
Youden’s statistic or the Euclidian distance method, however
the Youden’s defined model achieved a better model fit by
area under the curve comparison, (c=0.795) versus the
Euclidean method (c=0.76) and therefore was selected as the
final model method. Individuals who were positive for KIF5A
had 9.56 times the odds of asbestos exposure, while those
who were positive for KIF18A had 0.29 times the odds of
asbestos exposure compared to those who were negative for
each biomarker, and for each additional year of age individuals
had 1.05 times the odds of asbestos exposure (Table 3). KIF5A
positive status had a sensitivity of 85.35%, specificity of
47.56%, positive predictive value of 66.27%, and negative
predictive value of 72.9% for asbestos exposure. KIF18A
negative status had a sensitivity of 85.35%, specificity of
39.63%, positive predictive value of 69.15%, and negative
predictive value of 63.06%.

Discussion

Previous studies have indicated that kinesin proteins are
overexpressed in several types of cancers and involved in
tumorigenesis and/or metastasis of breast cancer, renal cell
carcinoma, nervous system tumors, lung tumors,
retinoblastoma tumors, and cervical cancer, and thus might be
useful targets for diagnosis and treatment [5,22-24]. KIF18A
has been shown to be essential in the congression of
chromosomes and the accurate alignment of the spindle
equator and to suppress kinetochore movements during
mitosis [25]. KIF18A overexpression has been positively related
to tumor size and clinical tumor-node-metastasis in
hepatocellular carcinoma (HCC), tumor grade and metastasis
in breast cancer, and tumor stage, lymphatic invasion, lymph
node metastasis, venous invasion, and peritoneal
dissemination in colorectal cancer (CRC) as well as poor clinical
outcomes [16-19].

In our present study we show that KIF18A serum
concentrations are significantly decreased in asbestos-exposed
individuals compared to unexposed controls, but that the
relationship between KIF18A and asbestos exposure varies
with age. The inverse association between KIF18A serum levels
and asbestos exposure is greatest at lower ages in this cohort
(40 to 60 years of age) and becomes non-significant by later
ages (69+). Lung is one of the few normal tissue types where
KIF18A is detectable and this decrease in its serum
concentration in asbestos-exposed individuals may signal an
early disruption of its expression and function in the lung
tissue. Interestingly the association was strongest in younger
aged individuals and not significant in older individuals, which
may indicate differences in physiologic responses to potential
respiratory insult/damage or an already present adaptive
response in older individuals. While less is known about the
role of KIF5A and its potential relationship to disease, our
present study has shown significantly increased serum
concentrations of KIF5A in asbestos-exposed individuals
compared to unexposed controls. This increase in serum KIF5A
concentrations may signal an increase in cell activity and/or
cell turnover as an early response to asbestos exposure. Both
of these changes in KIF18A and KIF5A serum concentrations
may be related to early changes that precede development of
disease as increased KIF5A concentrations and decreased
KIF18A concentrations were found to distinguish asbestos
cancer patients from non-cancer controls in a subset of
occupationally exposed Finnish asbestosis patients [20].

We additionally found that serum p53 autoantibody
concentration was not related to asbestos exposure in this
cohort of Italian workers. This finding is in contrast to the
borderline significant association between p53 autoantibodies
and cumulative asbestos found in a cohort of Finnish
asbestosis patients [10]. p53 autoantibodies were also not
significantly associated with smoking status, which was also
found in the cohort of Finnish asbestosis patients [10]. Given
the well-established relationship between p53 mutations and
p53 autoantibodies and numerous cancers including asbestosrelated
cancers, our finding of an absence of a relationship to
asbestos exposure may indicate that p53 mutations and subsequent autoantibody formation occur nearer to the
development of malignant changes and overt disease.

Strengths of this study include the investigation of two new
potential biomarkers of asbestos exposure and potentially
early effect in the disease process as well as an examination of
the potential relationship between p53 autoantibodies and
asbestos exposure in a new cohort including occupationally
exposed workers with a relatively long average cumulative
exposure to asbestos. We were additionally able to examine
the potential impact of smoking upon these relationships. Our
analyses found consistent magnitude and strength of
associations for KIF5A, a potential inverse association of
KIF18A, and lack of association for p53 autoantibodies with
asbestos exposure utilizing different approaches, lending
further confidence to our findings. This research does however
have several limitations including highly variable data on
smoking that necessitated use of current/ex/never
classifications, limited data on actual asbestos exposures
consisting only of years of presumed exposure, and only one
blood measurement used to quantify representative
biomarker levels for each individual. Additionally, while the
control group did contain a number of other blue collar and
white collar workers it was largely composed of healthcare
workers who may have differed from asbestos workers on
other unmeasured confounders such as diet, exercise, and
other lifestyle factors, and exposure to other occupational or
environmental pollutants with health risks.

Conclusion

The results from this study suggest that KIF5A, and possibly
KIF18A, could serve as useful biomarkers of asbestos exposure
and may signal early changes in cellular functioning that could
precede the development of asbestos related disease. While
the findings for KIF5A were consistent, the potentially varying
association observed for KIF18A and asbestos exposure by age
may limit its utility as a reliable biomarker. More research is
needed to clarify the potential relationships of these proteins
to asbestos exposure to determine if the associations found in
this population of occupationally exposed individuals are
consistent in other occupationally exposed populations as well
as possibly in individuals who would only be environmentally
exposed to asbestos. Additional studies could also help to
clarify the optimal cut point of each kinesin for the molecule to
serve as a useful tool for screening. Studies should also be
conducted to assess a potential relationship of these
molecules to the development of asbestos related disease.
This would help to broaden the understanding of the potential
role of these kinesins in response to asbestos exposure as
potential markers of early effect.

Acknowledgements

This work was supported in part by grants from NIOSH (R01-
OH07590 and R21-OH010311).